Mohammad Shoab, Abdullah Shawan Alotaibi*
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5765-5781, 2022, DOI:10.32604/cmc.2022.021941
Abstract Deep Reinforcement Learning (DRL) is a class of Machine Learning (ML) that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently. DRL has been used in many application fields, including games, robots, networks, etc. for creating autonomous systems that improve themselves with experience. It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially. Therefore, a novel query routing approach called Deep Reinforcement Learning based Route Selection… More >
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